{"id":"https://openalex.org/W2282347063","doi":"https://doi.org/10.1109/jstsp.2015.2503260","title":"A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors","display_name":"A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors","publication_year":2015,"publication_date":"2015-11-23","ids":{"openalex":"https://openalex.org/W2282347063","doi":"https://doi.org/10.1109/jstsp.2015.2503260","mag":"2282347063"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2015.2503260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2015.2503260","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://lirias.kuleuven.be/handle/123456789/515914","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003192020","display_name":"Nico Vervliet","orcid":"https://orcid.org/0000-0003-3006-4155"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Nico Vervliet","raw_affiliation_strings":["Katholieke Universiteit Leuven, Leuven, Flanders, BE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Katholieke Universiteit Leuven, Leuven, Flanders, BE","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009006026","display_name":"Lieven De Lathauwer","orcid":"https://orcid.org/0000-0001-5562-5014"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Lieven De Lathauwer","raw_affiliation_strings":["Katholieke Universiteit Leuven, Leuven, Flanders, BE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Katholieke Universiteit Leuven, Leuven, Flanders, BE","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99464096"],"apc_list":null,"apc_paid":null,"fwci":1.7194,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.8,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"10","issue":"2","first_page":"284","last_page":"295"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.9735999703407288,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9729999899864197,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6617100834846497},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5875463485717773},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5600240230560303},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4944028854370117},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48029443621635437},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.4730973541736603},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4640820026397705},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4538891613483429},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45005014538764954},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4386240839958191},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.4262882471084595},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4224390387535095},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3983113765716553},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3621620833873749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24146580696105957},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.0927649736404419}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6617100834846497},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5875463485717773},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5600240230560303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4944028854370117},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48029443621635437},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.4730973541736603},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4640820026397705},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4538891613483429},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45005014538764954},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4386240839958191},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.4262882471084595},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4224390387535095},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3983113765716553},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3621620833873749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24146580696105957},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0927649736404419},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstsp.2015.2503260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2015.2503260","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/515914","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/515914","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing, vol. 10 (2), Art.No. 2, (284-295)","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/515914","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/515914","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing, vol. 10 (2), Art.No. 2, (284-295)","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3207356259","display_name":null,"funder_award_id":"2007\u20132013","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G3509822617","display_name":null,"funder_award_id":"339804","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G393177772","display_name":null,"funder_award_id":"FP7 2007-2013","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4180450515","display_name":null,"funder_award_id":"IUAPP7/19","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G485065284","display_name":null,"funder_award_id":"PFV/10/002","funder_id":"https://openalex.org/F4320321454","funder_display_name":"Belgian Federal Science Policy Office"},{"id":"https://openalex.org/G5301324184","display_name":null,"funder_award_id":"P7/19","funder_id":"https://openalex.org/F4320321454","funder_display_name":"Belgian Federal Science Policy Office"},{"id":"https://openalex.org/G5702163051","display_name":null,"funder_award_id":"FP7/2007","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G6057626455","display_name":null,"funder_award_id":"CoE PFV/10/002","funder_id":"https://openalex.org/F4320322308","funder_display_name":"KU Leuven"},{"id":"https://openalex.org/G6182351167","display_name":null,"funder_award_id":"PFV/10/002","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G6734414162","display_name":null,"funder_award_id":"IUAP P7","funder_id":"https://openalex.org/F4320321454","funder_display_name":"Belgian Federal Science Policy Office"},{"id":"https://openalex.org/G7433218590","display_name":"Tensor-based study of data similarity.","funder_award_id":"G088114N","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G779967509","display_name":null,"funder_award_id":"IUAP P7/19/","funder_id":"https://openalex.org/F4320321454","funder_display_name":"Belgian Federal Science Policy Office"},{"id":"https://openalex.org/G8373550790","display_name":"Block Term Decompositions of Higher-Order Tensors: Uniqueness, Computation and Links with Structured Matrices.","funder_award_id":"G083014N","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G8829997307","display_name":null,"funder_award_id":"PFV/10/002","funder_id":"https://openalex.org/F4320322308","funder_display_name":"KU Leuven"},{"id":"https://openalex.org/G8863810249","display_name":null,"funder_award_id":"C1 project","funder_id":"https://openalex.org/F4320322308","funder_display_name":"KU Leuven"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320321454","display_name":"Belgian Federal Science Policy Office","ror":"https://ror.org/01fapfv42"},{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"},{"id":"https://openalex.org/F4320322308","display_name":"KU Leuven","ror":"https://ror.org/05f950310"},{"id":"https://openalex.org/F4320327336","display_name":"Vlaamse regering","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W19621276","https://openalex.org/W50417968","https://openalex.org/W111803032","https://openalex.org/W182774073","https://openalex.org/W650854417","https://openalex.org/W1491622225","https://openalex.org/W1568668329","https://openalex.org/W1682665945","https://openalex.org/W1697075315","https://openalex.org/W1755563775","https://openalex.org/W1967077133","https://openalex.org/W1974785908","https://openalex.org/W1980147176","https://openalex.org/W1989786408","https://openalex.org/W1989811026","https://openalex.org/W1994616650","https://openalex.org/W1999352252","https://openalex.org/W2010417247","https://openalex.org/W2018347513","https://openalex.org/W2022242697","https://openalex.org/W2024165284","https://openalex.org/W2024166170","https://openalex.org/W2030790903","https://openalex.org/W2031460246","https://openalex.org/W2033154334","https://openalex.org/W2035433001","https://openalex.org/W2038920431","https://openalex.org/W2047680880","https://openalex.org/W2057503509","https://openalex.org/W2058812752","https://openalex.org/W2063385922","https://openalex.org/W2066392792","https://openalex.org/W2070013413","https://openalex.org/W2071729267","https://openalex.org/W2077305791","https://openalex.org/W2086064090","https://openalex.org/W2086261631","https://openalex.org/W2095984592","https://openalex.org/W2101641981","https://openalex.org/W2103392911","https://openalex.org/W2108138101","https://openalex.org/W2113651538","https://openalex.org/W2119412403","https://openalex.org/W2119741678","https://openalex.org/W2121739212","https://openalex.org/W2132267493","https://openalex.org/W2137515395","https://openalex.org/W2138996475","https://openalex.org/W2141280932","https://openalex.org/W2150059498","https://openalex.org/W2199011767","https://openalex.org/W2490308495","https://openalex.org/W2952464298","https://openalex.org/W2964106499","https://openalex.org/W3098973674","https://openalex.org/W3101742238","https://openalex.org/W3123607434","https://openalex.org/W4244670803","https://openalex.org/W4301014524","https://openalex.org/W6600828528","https://openalex.org/W6607482675","https://openalex.org/W6629379589","https://openalex.org/W6637461557","https://openalex.org/W6680610060"],"related_works":["https://openalex.org/W2569661359","https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2897298721","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W2219450347","https://openalex.org/W3114815494","https://openalex.org/W4287549300"],"abstract_inverted_index":{"For":[0],"the":[1,12,50,75,81,98,110,124],"analysis":[2],"of":[3,14,21,52,79,115,126],"large-scale":[4,43],"datasets":[5],"one":[6],"often":[7],"assumes":[8],"simple":[9],"structures.":[10],"In":[11],"case":[13],"tensors,":[15,44],"a":[16,19,25,127,133,140,144],"decomposition":[17,32,60,99,125],"in":[18,139],"sum":[20],"rank-1":[22],"terms":[23],"provides":[24],"compact":[26],"and":[27,70,113,132],"informative":[28],"model.":[29],"Finding":[30],"this":[31],"is":[33,121],"intrinsically":[34],"more":[35],"difficult":[36],"than":[37],"its":[38],"matrix":[39],"counterpart.":[40],"Moreover,":[41],"for":[42],"computational":[45,76],"difficulties":[46],"arise":[47],"due":[48],"to":[49,73,104],"curse":[51],"dimensionality.":[53],"The":[54,119],"randomized":[55],"block":[56,92],"sampling":[57],"canonical":[58],"polyadic":[59],"method":[61],"presented":[62],"here":[63],"combines":[64],"increasingly":[65],"popular":[66],"ideas":[67],"from":[68,89],"randomization":[69],"stochastic":[71],"optimization":[72],"tackle":[74],"problems.":[77],"Instead":[78],"decomposing":[80],"full":[82],"tensor":[83,131,138],"at":[84],"once,":[85],"updates":[86],"are":[87],"computed":[88],"small":[90],"random":[91],"samples.":[93],"Using":[94],"step":[95],"size":[96],"restriction":[97],"can":[100],"be":[101],"found":[102],"up":[103],"near":[105],"optimal":[106],"accuracy,":[107],"while":[108],"reducing":[109],"computation":[111],"time":[112],"number":[114],"data":[116],"accesses":[117],"significantly.":[118],"scalability":[120],"illustrated":[122],"by":[123],"synthetic":[128],"8":[129],"TB":[130],"real":[134],"life":[135],"12.5":[136],"GB":[137],"few":[141],"minutes":[142],"on":[143],"standard":[145],"laptop.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}